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Atomic Structural Analysis of Nanowire Defects and Polytypes Enabled Through Cross-Sectional Lattice Imaging

Authors

  • Eric R. Hemesath,

    1. Department of Materials Science and Engineering, Northwestern University, 2220 N Campus Dr., Evanston IL 60208, USA
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  • Daniel K. Schreiber,

    1. Department of Materials Science and Engineering, Northwestern University, 2220 N Campus Dr., Evanston IL 60208, USA
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  • Christian F. Kisielowski,

    1. National Center for Electron Microscopy, Lawrence Berkeley National Laboratory, Berkeley CA 94720, USA
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  • Amanda K. Petford-Long,

    1. Department of Materials Science and Engineering, Northwestern University, 2220 N Campus Dr., Evanston IL 60208, USA
    2. Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL 60439, USA
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  • Lincoln J. Lauhon

    Corresponding author
    1. Department of Materials Science and Engineering, Northwestern University, 2220 N Campus Dr., Evanston IL 60208, USA
    • Department of Materials Science and Engineering, Northwestern University, 2220 N Campus Dr., Evanston IL 60208, USA.
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Abstract

Correlated transmission electron microscopy imaging, electron diffraction, and Raman spectroscopy are used to investigate the structure of Si nanowires with planar defects. In addition to plan-view imaging, individual defective nanowires are imaged in axial cross-section at specific locations selected in plan-view imaging. This correlated characterization approach enables definitive identification of complex defect structures that give rise to diffraction patterns that have been misinterpreted in the literature. Conclusive evidence for the 9R Si polytype is presented, and the atomic structure of this phase is correlated with kinematically-forbidden reflections in Si diffraction patterns. Despite striking similarities between imaging and diffraction data from twinned nanowires and the 9R polytype, clear distinctions between the structures can be made. Finally, the structural origins of ⅓{422} reflections in Si [111] diffraction patterns are identified.

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